#Kaggle-dataset
Showing 15 of 15 repositories tagged #kaggle-dataset, ranked by stars
Exploratory data analysis πusing python πof used car π database taken from βπππππ
Retrieve all historical candlestick data from crypto exchange Binance and upload it to Kaggle.
A professional TF-IDF + Logistic Regression style-risk classifier for educational fake-news detection, with a Streamlit dashboard, honest evaluation, uncertainty handling, and leakage analysis.
A model which uses your social media posting predict your MBTI personality type.
Data Extraction (from https://stats.nba.com) and Processing Scripts to Produce the NBA Database on Kaggle (https://kaggle.com/wyattowalsh/basketball)
Access data, statistics, and visualizations for New York's electricity grid.
Predict the toxicity rating of comment made by the user.
Kaggle Kernels (Python, R, Jupyter Notebooks)
A multiprocessing webscraper for Coursera.org to build a dataset for all courses with details like ratings, difficulty, etc.
A machine learning project that predicts Chronic Kidney Disease using patient medical data. The system applies data preprocessing, feature encoding, and classification models in Python to support early disease detection and healthcare decision-making.
This project demonstrates the application of machine learning techniques to predict house prices based on various features. By analyzing the dataset, preprocessing the data, and selecting an appropriate model, we were able to achieve a high level of accuracy in predicting house prices. The trained model can be further refined and deployed.
Being able to perform gameplay analysis of NBA players, NBA Predictive Analytics is a basketball coach's new best friend.
Real-time facial emotion recognition is a technology that uses computer vision and machine learning to analyze a person's facial expressions in real-time and determine their emotional state.
Pipeline for Automated Updates of Kaggle's "Formula 2 Dataset"
End-to-end cafΓ© inventory project: clean transaction data, build daily item-level demand series, backtest strong baseline forecasters, generate next-30-day demand forecasts, convert forecasts into safety stock + reorder points, and validate policies with Monte Carlo stockout-risk simulations, wrapped in a Streamlit dashboard.